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What is JoVE Visualize?

An artificial intelligence accelerated virtual screening platform for drug discovery

Affiliations
  • 1Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • 2Institute for Protein Design, University of Washington, Seattle, WA, USA.
  • 3Howard Hughes Medical Institute, Department of Pharmacology, University of Washington, Seattle, WA, USA.
  • 4Brain Science Institute, Korea Institute of Science and Technology, Seoul, Republic of Korea.
  • 5KIST-SKKU Brain Research Center, SKKU Institute for Convergence, Sungkyunkwan University, Suwon, Republic of Korea.
  • 6Department of Chemistry, University of Washington, Seattle, WA, USA.
  • 7Department of Pharmacology, University of California Davis, Davis, CA, USA.
  • 8Department of Physiology and Membrane Biology, University of California Davis, Davis, CA, USA.
  • 9Department of Anesthesiology and Pain Medicine, University of California Davis, Sacramento, CA, USA.
  • 10Howard Hughes Medical Institute, Department of Pharmacology, University of Washington, Seattle, WA, USA. nzheng@uw.edu.
  • 11Department of Biochemistry, University of Washington, Seattle, WA, USA. dimaio@u.washington.edu.
  • 12Institute for Protein Design, University of Washington, Seattle, WA, USA. dimaio@u.washington.edu.

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September 5, 2024

Abstract

Structure-based virtual screening is a key tool in early drug discovery, with growing interest in the screening of multi-billion chemical compound libraries. However, the success of virtual screening crucially depends on the accuracy of the binding pose and binding affinity predicted by computational docking. Here we develop a highly accurate structure-based virtual screen method, RosettaVS, for predicting docking poses and binding affinities. Our approach outperforms other state-of-the-art methods on a wide range of benchmarks, partially due to our ability to model receptor flexibility. We incorporate this into a new open-source artificial intelligence accelerated virtual screening platform for drug discovery. Using this platform, we screen multi-billion compound libraries against two unrelated targets, a ubiquitin ligase target KLHDC2 and the human voltage-gated sodium channel Na1.7. For both targets, we discover hit compounds, including seven hits (14% hit rate) to KLHDC2 and four hits (44% hit rate) to Na1.7, all with single digit micromolar binding affinities. Screening in both cases is completed in less than seven days. Finally, a high resolution X-ray crystallographic structure validates the predicted docking pose for the KLHDC2 ligand complex, demonstrating the effectiveness of our method in lead discovery.